Optimizing Ambulance Transport of Hemodialysis Patients to the Emergency Department: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Dialysis patients who require ambulance transport to the emergency department ("ambulance-ED") may subsequently require timely dialysis in a monitored setting ("urgent dialysis"). OBJECTIVE: The purpose of this study was to develop and internally validate a risk prediction model for urgent dialysis based on patient characteristics at the time of paramedic assessment before ambulance-ED. DESIGN: Cohort Study. SETTING: Region of Nova Scotia, Canada, covered by a single emergency medical services provider. PATIENTS: Thrice-weekly hemodialysis patients who initiated dialysis between 2009 and 2013 (follow-up to 2015) and experienced one or more ambulance-ED events. MEASUREMENTS: The primary outcome ("urgent dialysis") was defined as dialysis within 24 hours of an ambulance-ED in a monitored setting or dialysis within 24 hours of an ambulance-ED with an initial ED potassium of >6.5 mmol/L. Predictors of urgent dialysis based on paramedic assessment before ambulance-ED included presenting complaint, vital signs and time from last dialysis to ambulance dispatch. METHODS: Associations with urgent dialysis were analyzed using logistic regression from which a risk prediction model was created. The model was internally validated using bootstrapping and model performance was assessed by discrimination and calibration. RESULTS: Among 197 patients, there were 624 ambulance-ED events and 87 episodes of urgent dialysis. Weakness as a presenting complaint (odds ratio [OR]: 4.62, 95% confidence interval [CI]: 1.23-17.29), >24 hours since last dialysis (OR: 2.09, 95% CI: 1.15-3.81), and vital signs, including heart rate <60 beats/minute (OR: 3.06, 95% CI: 1.09-8.61), oxygen saturation <90% (OR: 3.04, 95% CI: 1.55-5.94), elevated respiratory rate (≥20 breaths/min), and systolic blood pressure>160 mmHg, were associated with urgent dialysis after ambulance-ED. A risk prediction model incorporating these variables had very good discrimination (C-statistic: 0.81, 95% CI: 0.76-0.86). The negative predictive value was 93.6% using the optimal cut point. Of patients who were predicted to need urgent dialysis but were transported to a facility incapable of providing it, 31% were re-transported for urgent dialysis. LIMITATIONS: Findings of our study may not be generalizable to other centers where the practice of ambulance transfer and availability of monitored dialysis may differ, and data were lacking for potential missed dialysis sessions or changes in routine dialysis scheduling. CONCLUSIONS: Patient characteristics at the time of paramedic assessment are associated with urgent dialysis after ambulance-ED. This risk prediction model has the potential to guide dialysis patient transport to dialysis-capable facilities when needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".